Antecedents and Consequences of Eco‐Control Deployment: Evidence from Canadian Manufacturing Firms
Bibliographic record
Abstract
Abstract Environmental issues have become an important consideration for a growing number of organizations. Eco‐control may represent a valuable tool to help organizations address such issues. The aim of this study is to provide an overview of the eco‐control practices adopted by Canadian organizations and to understand the antecedents and consequences of their adoption. More specifically, this study examines (i) the extent to which eco‐control practices are deployed within organizations, (ii) the factors and motivations that lead organizations to implement eco‐control practices, and (iii) the impact of adoption on firms’ managerial and operational environmental actions as well as on environmental and economic performance. Using survey data from a sample of 249 Canadian manufacturing firms, this article shows that environmental missions, environmental policies, environmental strategic planning, environmental budgets and environmental performance indicators are the most frequently adopted eco‐control practices among the investigated firms, while environmental incentives seem to be less frequently adopted. The results of this study also suggest that competitive and ethical motivations as well as size, environmental exposure and stakeholder pressure are all important factors in explaining eco‐control practice adoption by Canadian manufacturing firms. Moreover, the results of this study show that organizations that have undertaken more intensive managerial and operational environmental actions have also adopted more intensive eco‐control practices. Organizations adopting more intensive eco‐control practices perform better both environmentally and economically performance than firms adopting less intensive eco‐control practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".